ABSTRACTThis study aims to forecast the S&P 500 ESG index using the mixture model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long short‐term memory (LSTM) prediction models. CEEMDAN enables decomposing the index's original return series into different intrinsic mode functions (IMFs) and a residual series. The decomposed IMFs are then regrouped into aggregate series depicting high frequency and medium frequency, while the residual series represent the trend component. LSTM algorithm is used on the aggregated series to obtain predicted values of the same. The study compares different prediction algorithms to identify their performance and explore the predictive power of the hybrid models.

ABSTRACTIn this article, we evaluate the predictive power of multiple machine learning methods using different sets of information, such as location, amenities, socioeconomic characteristics, and available infrastructure nearby, in both residential and commercial real estate markets. This analysis allows us to understand what type of information is the most relevant for each market, which methods are best suited for certain explanatory variables, and the degree of complementarity among different covariates. Our results indicate that the combination of multiple data sources consistently leads to better forecasting and that flexible machine learning models outperform linear regression or spatial methods by taking advantage of the complex interactions between explanatory variables of different sources. From a substantive point of view, we found that residential sale markets have a higher prediction error compared with their rent counterparts, with house sales being the market with the largest estimation error. In terms of the explanatory power of different information sets in different markets, we observe that socioeconomic and location variables have the highest impact on the prediction for sale markets and that, in relative terms, amenities and proximity to places of interest are more important for rental than sale residential markets.

ABSTRACTWe investigate the performance of dynamic factor model nowcasting with preselected predictors in a mixed‐frequency setting. The predictors are selected via the elastic net as it is common in the targeted predictor literature. A simulation study and an application to empirical data are used to evaluate different strategies for variable selection, the influence of tuning parameters, and to determine the optimal way to handle mixed‐frequency data. We propose a novel cross‐validation approach that connects the preselection and nowcasting step. In general, we find that preselecting provides more accurate nowcasts compared with the benchmark dynamic factor model using all variables. Our newly proposed cross‐validation method outperforms the other specifications in most cases.

Abstract Cotton, cotton yarn, and other cotton products have frequent price volatility, increasing the difficulty for industry participants to develop rational business decision plans. To support cotton textile industry decision‐makers, we apply data mining methods to extract the main influencing factors affecting cotton yarn futures prices from big data and build a probabilistic forecasting model for cotton yarn price volatility with uncertainty assessment. Based on Explainable Artificial Intelligence (XAI) and data‐driven perspectives, we use the LassoNet algorithm to extract 18 features most relevant to the target variable from the massive data and visualize the importance values of the selected features to improve the reliability. Moreover, by combining conformal forecasting (CP) with quantile regression (QR), the uncertainty measure of the point estimation results of the long and short‐term memory (LSTM) model is applied to improve the application value of the model. Finally, SHAP (SHapley Additive exPlanations) is introduced to analyze the SHAP values of the input features on the output results and to explore in depth the interaction and mechanism of action between the input features and the target variables to improve the explainability of the model. Our model provides a “big data‐forecasting model‐decision support” decision paradigm for real‐world problems.

Abstract Forecasting the maritime economics index, including container volume and Baltic Panamax Index, is essential for long‐term planning and decision‐making in the shipping industry. However, studies on container volume prediction are not sufficient, and the bulk freight index has highly fluctuating characteristics, which pose a challenge in long‐term prediction. This study proposes a new hybrid framework for the long‐term prediction of the maritime economics index. The framework consists of time‐series decomposition to break down a time‐series into several components (trend, seasonality, and residual), a two‐stage attention mechanism that prioritizes important variables to increase long‐term prediction accuracy and a long short‐term memory network that predicts and combines all components to derive the final predictive outcome. Extensive experiments are conducted using the container volume data, bulk freight index data, and various external variables. The proposed framework achieved a better predictive performance than existing time‐series methods, including conventional machine learning and deep learning‐based models, in the long‐term prediction of container volume and the Baltic Panamax Index. Hence, the proposed method can help in decision‐making through accurate long‐term predictions of the maritime economics index.

Abstract This paper proposes a method that uses mean square error (MSE) and model confidence set (MCS) as the loss function of back‐propagation neural network (BPNN), aiming to train and find a generalized autoregressive conditional heteroskedastic (GARCH) model that has the best forecasting performance of a time series. Combining MSE and the p‐value of MCS can not only estimate better parameters for the GARCH models but also find the best GARCH model to forecast the volatility of a time series. Meanwhile, we divide a time series into several parts and use each part as the input of the BPNN. Through the BPNN, each part of the time series will be turned into several forecasting values. These values will be used to calculate the MSE and the p‐value of MCS, which will then be used to update the parameters of the BPNN. In the end, we use MCS to choose the best GARCH model among the trained GARCH models and compare this method with maximum likelihood estimation (MLE) and the generalized least squares estimation (GLS). The result shows that the p‐value of MCS of the best model estimated by this method is higher than the p‐value of MCS of the best model estimated by MLE and GLS. According to the theory of MCS, a model that has a larger p‐value does have a better forecasting performance. The method proposed by this paper can provide a potential application of neural network in GARCH model forecasting and estimation.

Abstract Market participants who need to trade a significant number of securities within a given period can face high transaction costs. In this paper, we document how improvements in intraday liquidity forecasts can help reduce total transaction costs. We compare various approaches for forecasting intraday transaction costs, including autoregressive and machine learning models, using comprehensive ultra‐high‐frequency limit order book data for a sample of NYSE stocks from 2002 to 2012. Our results indicate that improved liquidity forecasts can significantly decrease total transaction costs. Simple models capturing seasonality in market liquidity tend to outperform alternative models.

Abstract We evaluate whether professional forecasters incorporate valuable information from public discussions on social media. The study covers the case of inflation in Argentina for the period 2016–2022. We find solid evidence consistent with inattention. A simple indicator of attention to inflation on social media is shown to anticipate professional forecast errors. A one standard deviation increment in the indicator is followed by a rise of 0.4% in mean forecast errors in the subsequent month and by a cumulative increment of 0.7% over the next 6 months. Furthermore, social media content anticipates significant revisions in forecasts that target multiple months ahead inflation and calendar year inflation. These findings are different from previously documented forms of inattention. Consistent results are verified by implementing out‐of‐sample forecasts and using content from an alternative social network. The study has implications for the use of professional forecasts in the context of policymaking and sheds new evidence on the nature of imperfect information in macroeconomics.

Abstract This paper aims to study the volatility forecasting of Chinese crude oil futures from the large‐scale variables perspective by considering both the information on international futures markets volatility and technical indicators of Chinese crude oil futures. We employ the dual feature processing method (LASSO‐PCA) by integrating least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA) to extract important factors of the large‐scale exogenous variables. Besides the traditional ordinary least squares (OLS) estimation, the nonlinear support vector regression (SVR) approach is employed to integrate with the LASSO‐PCA method. The empirical results show that both the OLS and SVR combined with LASSO‐PCA can improve the forecasting accuracy, especially SVR‐LASSO‐PCA owns the best forecasting performance. The analysis of the selected variables finds international futures volatility is chosen more frequently. These results are further validated through a series of robust experiments. Finally, the time difference between China and the United States is also considered in order to obtain more reasonable out‐of‐sample forecasting.

Abstract From the cross‐market perspective, this paper investigates crude oil volatility index (OVX) forecasts by proposing a hybrid method, which combines the data‐driven SVR technique and parametric models. In terms of parametric models, we utilize GARCH‐type models with jumps, and the forecasting effects of five non‐parametric jumps (including interday and intraday jump tests) of stock market are also explored. Empirical results show that our approach can substantially increase forecasting accuracy. In addition, the model confidence set test and robust test reaffirm the superiority of the novel hybrid method. From the assessment of economic significance, the advantages of the hybrid method for volatility index forecasting are further confirmed. All these findings imply that jumps of stock market can be helpful in forecasting OVX, especially after the introduction of the hybrid method. Our work can certainly provide a new insight for volatility forecasting and cross‐market research.